Phase 2 study evaluating intermittent and continuous linsitinib and weekly paclitaxel in patients with recurrent platinum resistant ovarian epithelial cancer
Bibliographic record
Abstract
BACKGROUND: Linsitinib, an oral, dual inhibitor of insulin-like growth factor-1 receptor and insulin receptor, in combination with weekly paclitaxel, may improve clinical outcomes compared with paclitaxel alone in patients with refractory or platinum-resistant ovarian cancer. PATIENTS AND METHODS: on Days 1, 8, and 15; Arm A) or continuous linsitinib (150mg twice daily) in combination with paclitaxel (Arm B), or paclitaxel alone (Arm C). Primary endpoint was progression-free survival (PFS); secondary endpoints included overall survival (OS), overall response rate (ORR), disease control rate (DCR), and safety/tolerability. RESULTS: A total of 152 women were randomized to treatment (n=51 Arm A; n=51 Arm B, n=50 Arm C). In combination with paclitaxel, neither intermittent linsitinib (median PFS 2.8months; 95% confidence interval [CI]:2.5-4.4) nor continuous linsitinib (median PFS 4.2months; 95% CI:2.8-5.1) improved PFS over weekly paclitaxel alone (median PFS 5.6months; 95% CI:3.2-6.9). No improvement in ORR, DCR, or OS in either linsitinib dosing schedule was observed compared with paclitaxel alone. Adverse event (AE) rates, including all-grade and grade 3/4 treatment-related AEs, and treatment-related AEs leading to discontinuation, were higher among patients receiving intermittent linsitinib compared with the other treatment arms. CONCLUSION: Addition of intermittent or continuous linsitinib with paclitaxel did not improve outcomes in patients with platinum-resistant/refractory ovarian cancer compared with paclitaxel alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".